Density-based semi-supervised clustering

Data Mining and Knowledge Discovery

Volumn 21, Issue 3, 2010, Pages 345-370

  Ruiz, Carlos ^a   Spiliopoulou, Myra ^b   Menasalvas, Ernestina ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

Constraint based clustering; Constraints; Density based clustering; Instance level constraints; Semi supervised clustering

Indexed keywords

CONSTRAINT-BASED; CONSTRAINTS; DENSITY-BASED CLUSTERING; INSTANCE LEVEL CONSTRAINTS; SEMI-SUPERVISED CLUSTERING;

DISTRIBUTED COMPUTER SYSTEMS;

CLUSTERING ALGORITHMS;

EID: 77958035523     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-009-0157-y     Document Type: Article

References (43)

1. Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high dimensional data for data mining applications. In: SIGMOD'98: proceedings ACM SIGMOD international conference on management of data, pp 94-105
2. Anand SS, Bell DA, Hughes JG (1995) The role of domain knowledge in data mining. In: CIKM '95: proceedings of the fourth international conference on information and knowledge management, pp 37-43
3. Angiulli F, Pizzuti C, Ruffolo M (2004) DESCRY: A density based clustering algorithm for very large data sets. In: IDEAL'04: proceedings of intelligent data engineering and automated learning, pp 203-210
4. Ankerst M, Breunig MM, Kriegel H-P, Sander J (1999) OPTICS: ordering points to identify the clustering structure. In: SIGMOD'99: proceedings of the 1999 ACM SIGMOD international conference on management of data, pp 49-60
5. Basu S, Banerjee A, Mooney RJ (2002) Semi-supervised clustering by seeding. In: ICML'02: proceedings international conference on machine learning, pp 19-26
6. Basu S, Banerjee A, Mooney RJ (2004a) Active semi-supervision for pairwise constrained clustering. In: SDM'04: proceedings of the 4th SIAM international conference
7. Basu S, Bilenko M, Mooney RJ (2004b) A probabilistic framework for semi-supervised clustering. In: KDD'04: proceedings of 10th international conference on knowledge discovery in databases and data mining, pp 59-68
8. Bennett K, Bradley P, Demiriz A (2000) Constrained K-means clustering. Technical report, Microsoft Research. MSR-TR-2000-2065
9. Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18(9):509-517
10. Bilenko M, Basu S, Mooney RJ (2004) Integrating constraints and metric learning in semisupervised clustering. In: ICML'04: proceedings of the 21th international conference on machine learning, pp 11-19
11. Bilenko M, Mooney RJ (2003) Adaptive duplicate detection using learnable string similarity measures. In: KDD'03: proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, New York, NY, pp 39-48
12. Cohn D, Caruana R, McCallum A (2003) Semi-supervised clustering with user feedback. Technical report TR2003-1892, Cornell University
13. Davidson I, Basu S (2005) Clustering with constraints. In: ICDM'05: Tutorial at the 5th IEEE international conference on data mining
14. Davidson I, Basu S (2006) Clustering with constraints: Theory and practice. In: KDD'06: Tutorial at the international conference on knowledge discovery in databases and data mining
15. Davidson I, Ravi SS (2005) Agglomerative hierarchical clusteringwith constraints: Theoretical and empirical results. In: PKDD'05: proceedings of principles of knowledge discovery from databases, pp 59-70
16. Davidson I, Ravi SS (2005) Clustering with constraints: Feasibility issues and the K-means algorithm. In: SIAM'05: society for industrial and applied mathematics international conference on data mining
17. Davidson I, Ravi SS, Ester M (2007) Efficient incremental constrained clustering. In: KDD'07: proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, pp 240-249
18. Davidson I,Wagstaff K, Basu S (2006) Measuring constraint-set utility for partitional clustering algorithms. In: PKDD'06: proceeding of principles of knowledge discovery from databases, pp 115-126
19. Demiriz A, Bennett KP, Embrechts MJ (1999) Semi-supervised clustering using genetic algorithms. In: ANNIE'99: Artificial neural networks in engineering, pp 809-814 (Pubitemid 30523784)
20. Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algortihm for discovering clusters in large spatial database with noise. In: KDD'96: proceedings of 2nd international conference on knowledge discovery in databases and data mining
21. Guha S, Rastogi R, Shim K (1998) CURE: An efficient clustering algorithm for large databases. In: SIGMOD '98: proceeding of the 1998 ACM SIGMOD international conference on management of data, pp 73-84
22. Gunopulos D,VazirgiannisM,HalkidiM(2006) From unsupervised to semi-supervised learning: Algorithms and evaluation approaches. In: SIAM'06: Tutorial at society for industrial and applied mathematics international conference on data mining
23. Halkidi M, Gunopulos D, Kumar N, Vazirgiannis M, Domeniconi C (2005) A framework for semi-supervised learning based on subjective and objective clustering criteria. In: ICDM'2005: proceedings of the 5th IEEE international conference on data mining, pp 637-640
24. Han J, Lakshmanan LVS, Ng RT (1999) Constraint-based, multidimensional data mining. Comput IEEE Computer Soc Press 32(8):46-50
25. Hinneburg A,Keim DA(1998) An efficient approach to clustering in large multimedia databases with noise. In: KDD'98: proceedings of 4th international conference on knowledge discovery in databases and data mining, pp 58-65
26. Karypis G, Hang E-H, Kumar V (1999) Chameleon: hierchachical clustering using dynamic modeling. IEEE Comput 12(8):68-75
27. Klein D, Kamvar SD, Manning C (2002) From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In: ICML'02: proceedings of the 19th international conference on machine learning, pp 307-314
28. Kopanas I, Avouris NM, Daskalaki S (2002) The role of domain knowledge in a large scale data mining projects. In: Vlahavas IP, Spyropoulos CD (eds) Methods and applications of artificial intelligence. Second hellenic conference on AI, SETN 2002, volume 2308 of Lecture Notes in Computer Science. Springer
29. Newman D, Hettich S, Blake C, Merz C (1998) UCI repository of machine learning databases
30. RandWM (1971) Objective criteria for the evalluation of clustering methods. J Am Stat Assoc 66:846-850
31. Ruiz C, Menasalvas E, Spiliopoulou M (2009) C-DenStream: using domain knowledge over a data stream. In: DS'0r97: proceedings of the international discovery science conference porto, Portugal, Oct 2009. to appear
32. Ruiz C, Spiliopoulou M, Menasalvas E (2006) User constraints over data streams. In: IWKDDS'06: proceedings of the 4th workshop on knowledge discovery from data streams at ECML/PKDD'06, pp 117-226
33. Ruiz C, Spiliopoulou M, Menasalvas E (2007a) C-DBSCAN: density-based clustering with constraints. In: RSFDGrC'07: proceedings of the international conference on rough sets, fuzzy sets, data mining and granular computing held by JRS'07
34. Ruiz C, Spiliopoulou M, Menasalvas E (2007b) Constraint-based query clustering. In: AWIC'07: proceedings of the 5th Atlantic web intelligence conference
35. Sheikholeslami G, Chatterjee S, Zhang A (1998) WaveCluster: A multi-resolution clustering approach for very large spatial databases. In: VLDB'98: proceedings of 24th international conference on very large data bases, pp 428-439
36. Vazirgiannis M, Halkidi M, Gunopoulos D (2003) Quality assessment and uncertainty handling in data mining. Springer, LNAI Series
37. Wagstaff K (2002) Intelligent clustering with instance-level constraints. PhD thesis, Universidad de Cornell
38. Wagstaff K, Cardie C (2000) Clustering with instance-level constraints. In: ICML'00: proceedings of 17th international conference on machine learning, pp 1103-1110
39. Wagstaff K, Cardie C, Rogers S, Schroedl S (2001) Constrained K-means clustering with background knowledge. In: ICML'01: proceedings of 18th international conference on machine learning, pp 577- 584
40. Wang W, Yang J, Muntz RR (1997) STING: A statistical information grid approach to spatial data mining. In: VLDB'97: proceedings of the 23rd international conference on very large data bases, pp 186-195
41. Xing EP, Ng AY, Jordan MI, Russell S (2003) Distance metric learning, with application to clustering with side-information. Adv Neural Inf Process Syst 15:505-512
42. Zaïane OR, Lee C-H (2002) Clustering spatial data in the presence of obstacles: A density-based approach. In: IDEAS'02: proceedings of the 2002 international symposium on database engineering and applications. IEEE Computer Society, Washington, DC, pp 214-223
43. Zaïane OR, Lee C-H (2002) Clustering spatial data when facing physical constraints. In: ICDM'02: proceedings of the 2002 IEEE international conference on data mining (ICDM'02). IEEE Computer Society, Washington, DC, p 737